Constraints on intrinsic alignment contamination of weak lensing surveys using the MegaZ-LRG sample

Abstract

Correlations between the intrinsic shapes of galaxies and the large-scale galaxy density field provide an important tool to investigate galaxy intrinsic alignments, which constitute the major potential astrophysical systematic in cosmological weak lensing (cosmic shear) surveys, but also yield insight into the formation and evolution of galaxies. We measure galaxy position-shape correlations in the MegaZ-LRG sample for more than 800 000 luminous red galaxies for comoving transverse separations of 0.3 < rp < 60 h-1 Mpc, making the first such measurement with a photometric redshift sample. In combination with a re-analysis of several spectroscopic SDSS samples, we constrain an intrinsic alignment model for early-type galaxies over long baselines in redshift (z ≲ 0.7) and luminosity (4 mag) with high statistical precision. We develop and test the formalism to incorporate photometric redshift scatter in the modelling of these observations. For rp > 6 h-1 Mpc, the fits to galaxy position-shape correlation functions are consistent with the scaling with rp and redshift of a revised, nonlinear version of the linear alignment model (Hirata & Seljak 2004) for all samples. An extra redshift dependence ∝ (1 + z)ηother is constrained to ηother = −0.3 ± 0.8 (1σ). To obtain consistent amplitudes for all data, an additional dependence on galaxy luminosity ∝ Lβ with is required. The normalisation of the intrinsic alignment power spectrum is found to be for galaxies at redshift 0.3 and r band magnitude of − 22 (k- and evolution-corrected to z = 0). Assuming zero intrinsic alignments for blue galaxies, we assess the bias on cosmological parameters for a tomographic CFHTLS-like lensing survey given our new constraints on the intrinsic alignment model parameter space. Both the resulting mean bias and its uncertainty are smaller than the 1σ statistical errors when using the constraints from all samples combined. The addition of MegaZ-LRG data is critical to achieving constraints this strong, reducing the uncertainty in intrinsic alignment bias on cosmological parameters by factors of three to seven.